CQ2RS: a new statistical approach to the response surface method for reliability analysis

Gayton, Nicolas; Bourinet, Jean‐Marc; Lemaire, Maurice

doi:10.1016/s0167-4730(02)00045-0

Cited by 174 publications

(81 citation statements)

References 12 publications

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“…RSM is also a effective tool in assessing the reliability of complex structures which requires a deal between reliability algorithms and mechanical methods used to model the mechanical behavior, and the interest of this method is that the user is allocated to choose and check the mechanical experiments [12,31]. RSM was used to explore interactions among parameters and predict the failure regions.…”

Section: Partial Least Square Regression Methods and Response Surface mentioning

confidence: 99%

Estimation of system reliability by using the PLS-regression based corrected response surface method

Liu

Jiang

Hulio

et al. 2016

EiN

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Section: Partial Least Square Regression Methods and Response Surface mentioning

confidence: 99%

Estimation of system reliability by using the PLS-regression based corrected response surface method

Liu

Jiang

Hulio

et al. 2016

EiN

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“…Among others, general response surfaces [19,20,21,22], neural networks [23] and support vector machines [24] have been used.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Emulation-Based Reliability Analysis

Hristov¹,

DiazDelaO²,

Kubiak³

et al. 2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Abstract. This paper presents an approximation method for performing reliability analysis with high fidelity computer codes at a reasonable computational cost. Complex models are common in science and engineering due to their ability to substitute costly and some times infeasible practical experiments. These models, however, suffer from high computational cost. This causes problems when performing sampling -based reliability analysis, since the failure modes of the system typically occupy a small region of the input space and thus relatively large sample sizes are required for the accurate estimation of their characteristics. The sequential sampling method proposed in this article, combines Gaussian process-based optimisation and Subset Simulation. Gaussian process emulators construct a statistical approximation to the output of the original code, which is both affordable to use and has its own measure of predictive uncertainty. Subset Simulation is used to efficiently populate those regions of the initial approximation which are likely to lead to the performance function exceeding a predefined critical threshold. Among all samples, the ones that are likely to contribute most to increasing the quality of the surrogate in the vicinity of the failure regions are selected, using Bayesian optimisation methods. The iterative nature of the method ensures that an arbitrarily accurate approximation of the failure region in performance space is developed at a reasonable computational cost. The presented method is applied to a number of benchmark problems.

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“…In subsequent papers, e.g. [8][9][10][11][12], the aspect that the response surface includes only a part of the random variables has not been further pursued and the response surface methods proposed in these papers are purely interpolation or regression methods.…”

Section: Introductionmentioning

confidence: 99%

“…They introduce the idea to reuse the information at formerly evaluated points. Later, Gayton et al [12] also consider the reuse of formerly evaluated points and elaborate an algorithm for iterative MPP estimates based on resampling of experiments. However, due to the use of a complete second order response surface, the computational effort is of O(n 2 ).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it is noted that global approximations are often constructed for parameter spaces that ignore constraints imposed by the physical nature of the problem. Proposals to circumvent these problems encompass iterative updating of the response surface [9][10][11][12], introduction of hyperplane approximations [16][17][18] and of artificial neural networks [19][20][21][22]. Hyperplane approximation methods show a tendency to link importance sampling methods and response surfaces, but are limited by the necessity to find evaluation points that lie on the failure hypersurface and by gradient computations.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of failure probabilities by local approximation of the limit state function

Proppe

2008

Structural Safety

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For failure probability estimates of large structural systems, the numerical expensive evaluations of the limit state function have to be replaced by suitable approximations. Most of the methods proposed in the literature so far construct global approximations of the failure hypersurface. Rather than concentrating on the construction of the failure hypersurface, an adaptive local approximation scheme for the limit state function that is based on the moving least squares method is proposed in this study. It integrates well with existing importance sampling schemes and yields both efficient and robust estimates of the failure probability.

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CQ2RS: a new statistical approach to the response surface method for reliability analysis

Cited by 174 publications

References 12 publications

Estimation of system reliability by using the PLS-regression based corrected response surface method

Estimation of system reliability by using the PLS-regression based corrected response surface method

Adaptive Emulation-Based Reliability Analysis

Estimation of failure probabilities by local approximation of the limit state function

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